IJCAI 2021 AI4AD Workshop
Artificial Intelligence for Autonomous Driving
August 20, 2021 | Montreal, Canada (Virtual)
Welcome to AI4AD
Autonomous driving provides a rich source of high-impact research problems for the broad artificial intelligence community across different fields such as computer vision, machine learning, robotics, language and speech, civil engineering, human-computer interaction, environmental science, and neuroscience. Further, full self-driving capability (“Level 5”) is far from solved and extremely complex, beyond the capability of any one institution or company, necessitating larger-scale communication and collaboration between researchers in different fields. The goal of this workshop is to embrace interdisciplinary knowledge in different fields of AI, from both academia and industry, to discuss how different fields can contribute to self-driving technology altogether and increase its social impact.
August 21, Thanks everyone for presenting and attending! We had a great workshop yesterday! Video recordings have been uploaded here and our YouTube channel
August 16, Please find our workshop in Green 3 area of the IJCAI virtual platform!
August 9, Full schedule of keynote and oral presentations has been announced here!
July 27, Video demo for accepted papers has been uploaded here!
June 27, Camera ready papers are available here! Video demo will be uploaded soon.
May 31, Paper decision has been sent out! A total of 12 submissions are accepted. Congratulations!
April 9, Paper submission opens. We feature two tracks: (1) short papers (≤ 4 pages); (2) regular papers.
March 25, Our workshop website is online.
March 15, Our workshop is accepted at IJCAI 2021!
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Vision-based perception and scene understanding for autonomous driving
Multi-modality sensor fusion for autonomous driving
End-to-end and real-time autonomous driving systems
Novel automotive sensors and their applications
Behavior prediction of pedestrians, vehicles, and animals
Self/semi/weakly-supervised learning, domain adaptation for self-driving
Multi-task learning in autonomous driving
Explainability and interpretability in autonomous driving
Robustness to out-of-distribution road scenes
Learning to drive via imitation learning
Uncertainty propagation through autonomous driving pipelines
Planning and control for autonomous driving
Cooperative and competitive multi-agent systems
Visual grounding and its application to autonomous driving
Visual-language navigation for self-driving
Audio-visual navigation for self-driving
Auditory Perception (detection, tracking, motion estimation, etc)
Brain-inspired autonomous control systems
Human factors in autonomous driving
AI ethics in autonomous driving
Autonomous driving datasets and benchmarks
Evaluation and metrics of autonomous driving tasks
Connected autonomous driving and vehicle-to-vehicle communication
Autonomous driving for traffic management and emission reduction